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Abstract:

The relevance of advertisements to a user's interests is improved. In one
implementation, the content of a web page is analyzed to determine a list
of one or more topics associated with that web page. An advertisement is
considered to be relevant to that web page if it is associated with
keywords belonging to the list of one or more topics. One or more of
these relevant advertisements may be provided for rendering in
conjunction with the web page or related web pages.

Claims:

1. A computer-implemented method comprising:a) determining, by a server
system including a least one processor on a network, a set of one or more
topics for a target document by calculating weighted terms for the target
document based on text within the target document, wherein the set of one
or more topics contains those of the weighted terms whose weight exceeds
a defined threshold;b) for each of a plurality of advertisements,
determining, by the server system, whether the advertisement is relevant
to the target document by analyzing a set of one or more topics,
previously provided from an advertiser as targeting information for the
advertisement, with respect to the set of one or more topics of the
target document;c) for each of the plurality of advertisements, making a
serving determination, by the server system, using at least the
determination of whether or not the advertisement is relevant to the
target document; andd) controlling, by the server system, serving of the
advertisement for presentation to a user via a client device using the
serving determination.

2. The computer-implemented method of claim 1 wherein analyzing a set of
one or more topics, previously provided from an advertiser as targeting
information for the advertisement, with respect to the set of one or more
topics of the target document, includes scoring a similarity between the
set of one or more topics of the advertisement and the set of one or more
topics of target document.

3. The computer-implemented method of claim 1 wherein the set of one or
more topics previously provided from an advertiser as targeting
information for the advertisement include at least one of (A) a keyword
and (B) a phrase, and wherein the set of one or more topics previously
provided from an advertiser as targeting information for the
advertisement were received via an ad campaign entry and management
component of the server system.

4. The computer-implemented method of claim 1 wherein the set of one or
more topics of the target document further includes at least one topic
from another document linked to the target document.

5. The computer-implemented method of claim 1 wherein the set of one or
more topics of the target document further includes at least one topic
from another document linked from the target document.

6. The computer-implemented method of claim 1 wherein the set of one or
more topics of the target document further includes anchor text in a link
from another document to the target document.

7. The computer-implemented method of claim 1 wherein the set of one or
more topics of the target document further includes text from queries to
a search engine that returned a search result including the target
document.

8. The computer-implemented method of claim 1 wherein the set of one or
more topics of the target document further includes text from queries to
a search engine that returned a search result including the target
document, which was subsequently selected by a user.

9. A computer-implemented method comprising:a) determining, by a server
system including a least one processor on a network, a set of one or more
topics for a target document by calculating weighted terms for the target
document based on text within the target document, wherein the set of one
or more topics includes a defined number of those of the weighted terms
with the highest weights among the weighted terms;b) for each of a
plurality of advertisements, determining, by the server system, whether
the advertisement is relevant to the target document by analyzing a set
of one or more topics, previously provided from an advertiser as
targeting information for the advertisement, with respect to the set of
one or more topics of the target document;c) for each of the plurality of
advertisements, making a serving determination, by the server system,
using at least the determination of whether or not the advertisement is
relevant to the target document; andd) controlling, by the server system,
serving of the advertisement for presentation to a user via a client
device using the serving determination.

10. The computer-implemented method of claim 9 wherein analyzing a set of
one or more topics, previously provided from an advertiser as targeting
information for the advertisement, with respect to the set of one or more
topics of the target document, includes scoring a similarity between the
set of one or more topics of the advertisement and the set of one or more
topics of target document.

11. The computer-implemented method of claim 9 wherein the set of one or
more topics previously provided from an advertiser as targeting
information for the advertisement include at least one of (A) a keyword
and (B) a phrase, and wherein the set of one or more topics previously
provided from an advertiser as targeting information for the
advertisement were received via an ad campaign entry and management
component of the server system.

12. The computer-implemented method of claim 9 wherein the set of one or
more topics of the target document further includes at least one topic
from another document linked to the target document.

13. The computer-implemented method of claim 9 wherein the set of one or
more topics of the target document further includes at least one topic
from another document linked from the target document.

14. The computer-implemented method of claim 9 wherein the set of one or
more topics of the target document further includes anchor text in a link
from another document to the target document.

15. The computer-implemented method of claim 9 wherein the set of one or
more topics of the target document further includes text from queries to
a search engine that returned a search result including the target
document.

16. The computer-implemented method of claim 9 wherein the set of one or
more topics of the target document further includes text from queries to
a search engine that returned a search result including the target
document, which was subsequently selected by a user.

17. A computer-implemented method comprising:a) determining, by a server
system including a least one processor on a network, weighted terms for a
target document based on text within the target document, wherein the
weight of each of the determined weighted terms is based on at least one
of (A) a frequency with which the term appears in the text of the target
document, and (B) and infrequency with which the term appears across a
collection of documents;b) for each of a plurality of advertisements,
determining, by the server system, whether the advertisement is relevant
to the target document by analyzing a set of one or more topics,
previously provided from an advertiser as targeting information for the
advertisement, with respect to the weighted terms;c) for each of the
plurality of advertisements, making a serving determination, by the
server system, using at least the determination of whether or not the
advertisement is relevant to the target document; andd) controlling, by
the server system, serving of the advertisement for presentation to a
user via a client device using the serving determination.

18. The computer-implemented method of claim 17 wherein analyzing a set of
one or more topics, previously provided from an advertiser as targeting
information for the advertisement, with respect to the weighted terms of
the target document, includes scoring a similarity between the set of one
or more topics of the advertisement and the weighted terms of the target
document.

19. The computer-implemented method of claim 17 wherein the set of one or
more topics previously provided from an advertiser as targeting
information for the advertisement include at least one of (A) a keyword
and (B) a phrase, and wherein the set of one or more topics previously
provided from an advertiser as targeting information for the
advertisement were received via an ad campaign entry and management
component of the server system.

20. Apparatus comprising:a) at least one processor; andb) at least one
storage device storing processor executable program instructions which,
when executed by the at least one processor, perform a method including1)
determining a set of one or more topics for a target document by
calculating weighted terms for the target document based on text within
the target document, wherein the set of one or more topics contains at
least one of (A) those of the weighted terms whose weight exceeds a
defined threshold, and (B) a defined number of those of the weighted
terms with the highest weights among the weighted terms,2) for each of a
plurality of advertisements, determining whether the advertisement is
relevant to the target document by analyzing a set of one or more topics,
previously provided from an advertiser as targeting information for the
advertisement, with respect to the set of one or more topics of the
target document,3) for each of the plurality of advertisements, making a
serving determination using at least the determination of whether or not
the advertisement is relevant to the target document, and4) controlling
serving of the advertisement for presentation to a user via a client
device using the serving determination.

[0003]The present invention relates generally to advertising and, more
particularly, to serving relevant advertisements by comparing
advertisers' targeting criteria to the content of media on which the
advertisements are to be published.

[0004]B. Description of Related Art

[0005]Advertising using traditional media, such as television, radio,
newspapers and magazines, is well known. Advertisers have used these
types of media to reach a large audience with their advertisements
("ads"). To reach a more responsive audience, advertisers have used
demographic studies. For example, advertisers may use broadcast events
such as football games to advertise beer and action movies to a younger
male audience. However, even with demographic studies and entirely
reasonable assumptions about the typical audience of various media
outlets, advertisers recognize that much of their ad budget is simply
wasted because the target audience is not interested in the ad they are
receiving.

[0006]Interactive media, such as the Internet, has the potential for
better targeting of advertisements. For example, some websites provide an
information search functionality that is based on query keywords entered
by the user seeking information. This user query can be used as an
indicator of the type of information of interest to the user. By
comparing the user query to a list of keywords specified by an
advertiser, it is possible to provide some form of targeted
advertisements to these search service users. An example of such a system
is the Adwords system offered by Google, Inc.

[0007]While systems such as Adwords have provided advertisers the ability
to better target ads, their effectiveness is limited to sites where a
user enters a search query to indicate their topic of interest. Most web
pages, however, do not offer search functionality and for these pages it
is difficult for advertisers to target their ads. As a result, often, the
ads on non-search pages are of little value to the viewer of the page and
are therefore viewed more as an annoyance than a source of useful
information. Not surprisingly, these ads typically provide the advertiser
with a lower return on investment than search-based ads, which are more
targeted.

[0008]It would be useful, therefore, to have methods and apparatus for
providing relevant ads for situations where a document is provided to an
end user, but not in response to an express indication of a topic of
interest by the end user (e.g., not responsive to the end user submitting
a search query).

SUMMARY OF THE INVENTION

[0009]Systems and methods consistent with the present invention address
this and other needs by identifying targeting information for an
advertisement, analyzing the content of a target document to identify a
list of one or more topics for the target document, comparing the
targeting information to the list of topics to determine if a match
exists, and determining that the advertisement is relevant to the target
document if the match exists.

[0010]Additional aspects of the present invention are directed to computer
systems and to computer-readable media having features relating to the
foregoing aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011]The accompanying drawings, which are incorporated in and constitute
a part of this specification, illustrate an embodiment of the invention
and, together with the description, explain the invention. In the
drawings,

[0012]FIG. 1 is a diagram illustrating an environment within which the
invention may be implemented;

[0013]FIG. 2 is a diagram functionally illustrating an advertising system
consistent with the invention;

[0014]FIG. 3 is a diagram illustrating apparatus with which the invention
may be implemented;

[0015]FIG. 4 is a flow diagram of an exemplary method for providing
relevant advertisements, consistent with the present invention; and

[0016]FIG. 5 is a sample target document.

DETAILED DESCRIPTION

[0017]The following detailed description of the invention refers to the
accompanying drawings. The detailed description does not limit the
invention. Instead, the scope of the invention is defined by the appended
claims and equivalents.

[0018]The present invention involves methods and apparatus for determining
advertisements that are relevant to a given document. In one
implementation, the document is a web page and the advertisements are
electronic files that are capable of being rendered on that web page. A
set, such as a list, of topics corresponding to the web page is generated
by analyzing the content of the web page. There are a variety of
techniques by which this may be performed, one of which is by computing a
term vector for the web page and selecting the top N terms from that
vector. The list of topics is compared to target information associated
with the advertisements (e.g., keywords specified for the advertisements)
to determine which of the advertisements are relevant to the web page.
Some or all of these relevant advertisements may then be associated with
the web page so that they may be rendered (e.g., displayed) with the web
page.

[0019]Those skilled in the art will recognize that many other
implementations are possible, consistent with the present invention.

[0020]A. Environment and Architecture

[0021]FIG. 1 is a diagram illustrating an environment within which the
invention may be implemented. The environment includes an advertiser 110,
an advertising system 120, an advertisement consumer 130, and an
advertising target 140.

[0022]Advertiser 110 may be the party that directly sells the goods or
services being advertised (e.g., Amazon.com) or an agent authorized to
act on the advertiser's behalf. The advertisement desired by advertiser
110 may exist in a variety of forms ranging from standard print
advertisements, online advertisements, audio advertisements, audio/visual
advertisements, or any other type of sensory message desired.

[0023]Advertising system 120 interfaces with both the advertiser 110 and
the advertisement consumer 130. It may perform a variety of functions, as
explained in more detail below in reference to FIG. 2. This invention may
be used with such an advertising system 120.

[0024]Advertisement consumer 130 is the entity that will issue a request
for advertisements to advertising system 120, obtain the advertisements
from advertising system 120, and present the advertisement to the
advertising target 140. Typically, the advertisement consumer is the
entity that provides the content with which the advertisement is to be
associated. In one implementation, the advertising consumer 130 is a
search engine, such as that employed by Google, Inc. at www.google.com.

[0025]Advertising target 140 is the individual (or set of individuals) who
ultimately receive the advertisement. In the case of visual
advertisements, for example, the advertisement target 140 is the person
who views the advertisement.

[0026]FIG. 2 is a diagram functionally illustrating an advertising system
consistent with the invention. The system includes an ad campaign entry
and management component 210, a tools component 220, a billing component
230, one or more databases 240, an ad consumer interface component 250,
an ad selection component 260, an ad ordering component 270, an ad
serving component 280, and a statistics engine component 290. If the
present invention is to be used with such an advertising system, it will
primarily concern ad selection component 260. To help understand the
invention, other components of the advertising system will be explained
below. Furthermore, although FIG. 2 shows a particular arrangement of
components constituting advertisement system 120, those skilled in the
art will recognize that not all components need be arranged as shown, not
all components are required, and that other components may be added to,
or replace, those shown.

[0027]Ad entry and management component 210 is the component by which the
advertiser enters information required for an advertising campaign and
manages the campaign. An ad campaign contains one or more advertisements
that are related in some manner. For example, the Ford Motor Company may
have an ad campaign for zero percent financing, which could contain a
series of advertisements related to that topic. Among the other things
that could be provided by an advertiser through ad entry and management
component 210 are the following: one or more advertising creatives
(simply referred to as "ads" or "advertisements"), one or more set of
keywords or topics associated with those creatives (which may be used as
targeting information for the ads), geographic targeting information, a
value indication for the advertisement, start date, end date, etc. The
data required for, or obtained by, ad entry and management component 210
resides in one of the databases 240.

[0028]Tools component 220 contains a variety of tools designed to help the
advertiser 110 create, monitor, and manage its campaigns. For example,
tools component 220 may contain a tool for helping advertiser 110
estimate the number of impressions an ad will receive for a particular
keyword or topic. Similarly, tools component 220 may be used to help
advertiser 110 generate a list of keywords or topics for a given
advertisement, or to generate additional keywords or topics based on
representative ones supplied by advertiser 110. Other possible tools may
be provided as well. Depending on the nature of the tool, one or more
databases 240 may be used to gather or store information.

[0029]Billing component 230 helps perform billing-related functions. For
example, billing component 230 generates invoices for a particular
advertiser 110 or ad campaign. In addition, billing component 230 may be
used by advertiser 110 to monitor the amount being expended for its
various campaigns. The data required for, or obtained by, billing
component 230 resides in a database 240.

[0030]Databases 240 contain a variety of data used by advertising system
120. In addition to the information mentioned above in reference to ad
entry and management system 210, databases 240 may contain statistical
information about what ads have been shown, how often they have been
shown, the number of times they have been selected, who has selected
those ads, how often display of the ad has led to consummation of a
transaction, etc. Although the databases 240 are shown in FIG. 2 as one
unit, one of ordinary skill in the art will recognize that multiple
databases may be employed for gathering and storing information used in
advertising system 120.

[0031]Ad consumer interface 250 is a component that interfaces with ad
consumer 130 to obtain or send information. For example, ad consumer 130
may send a request for one or more advertisements to ad consumer
interface 250. The request may include information such as the site
requesting the advertisement, any information available to aid in
selecting the advertisement, the number of ads requested, etc. In
response, ad consumer interface 250 may provide one or more
advertisements to ad consumer 130. In addition, ad consumer 130 may send
information about the performance of the advertisement back to the ad
system via the ad consumer interface 250. This may include, for example,
the statistical information described above in reference to a database
240. The data required for, or obtained by, ad consumer interface
component 250 resides in a database 240.

[0032]Ad selection component 260 receives a request for a specified number
of advertisements, coupled with information to help select the
appropriate advertisements. This information may include, for example, a
search query specified by an end user. Alternatively, or in addition, as
described in more detail below, this information may include data related
to the content of the page for which the advertisements are being
requested.

[0033]Ad ordering component 270 receives a list of relevant ads from ad
selection component 260 and determines a preference order in which they
should be rendered to an end user. For example, relevant ads may be
ordered based on the value indication associated with each ad. These
ordered ads may be provided to an ad serving component 280.

[0034]Ad serving component 280 receives an ordered list of ads from ad
ordering component 270, and formats that list into a manner suitable for
presenting to ad consumer 130. This may involve, for example, rendering
the ads into hypertext markup language (HTML), into a proprietary data
format, etc.

[0035]Statistics engine 290 contains information pertaining to the
selection and performance of advertisements. For example, statistics
engine 290 may log the information provided by ad consumer 130 as part of
an ad request, the ads selected for that request by ad selection
component 260, the order selected by ad ordering component 270, and the
presentation of the ads by ad serving component 280. In addition,
statistics engine 290 may log information about what happens with the
advertisement once it has been provided to ad consumer 130. This includes
information such as on what location the ad was provided, what the
response was to the advertisement, what the effect was of the
advertisement, etc.

[0036]FIG. 3 is a diagram illustrating an architecture in which the
present invention may be implemented. The architecture includes multiple
client devices 302, a server device 310, and a network 301, which may be,
for example, the Internet. Client devices 302 each include a
computer-readable medium 309, such as random access memory, coupled to a
processor 308. Processor 308 executes program instructions stored in
memory 309. Client devices 302 may also include a number of additional
external or internal devices, such as, without limitation, a mouse, a
CD-ROM, a keyboard, and a display. Thus, as will be appreciated by those
skilled in the art, the client devices may be personal computers,
personal digital assistances, mobile phones, content players, etc.

[0037]Through client devices 302, requestors 305 can communicate over
network 301 with each other and with other systems and devices coupled to
network 301, such as server device 310. Requestors 305 may, for example,
be advertisers 110, advertisement consumer 130, or advertising target
140.

[0038]Similar to client devices 302, server device 310 may include a
processor 311 coupled to a computer readable memory 312. Server device
310 may additionally include a secondary storage element, such as a
database 240.

[0039]Client processors 308 and server processor 311 can be any of a
number of well known micro-processors, such as processors from Intel
Corporation, of Santa Clara, Calif. In general, client device 302 may be
any type of computing platform connected to a network and that interacts
with application programs, such as a digital assistant or a "smart"
cellular telephone or pager. Server 310, although depicted as a single
computer system, may be implemented as a network of computer processors.

[0040]Memory 312 may contain a number of programs, such as the components
described above in reference to FIG. 2.

[0041]B. Operation

[0042]FIG. 4 is a flow diagram of an exemplary method for determining if
an advertisement is relevant to a document, consistent with the present
invention. As used herein, the term "document" includes any type of paper
or electronic document or file, including audio, video, image, text, etc.
That is, as will be appreciated by one skilled in the art, a "document"
as used in the specification is any machine-readable and machine-storable
work product. A document may be a file, a combination of files, one or
more files with embedded links to other files, etc. For the sake of
illustration, it may be understood that the process described herein
takes place as part of the ad selection component 260, although those
skilled in the art will recognize that it need not take place in that
component alone.

[0043]The exemplary method is not limited by the order shown in the flow
diagram. The process identifies targeting information for an
advertisement. (Stage 410). The targeting information may be in the form
of a list of keywords or phrases associated with the advertisement (e.g.,
"honda", "honda cars", "cars", etc.), as provided by advertiser 110
through ad campaign entry and management component 210. Alternatively, or
in addition, the targeting information may be determined algorithmically,
based on the content of the advertisement, the goods or services being
advertised, the targeting of other related advertisements, etc. For
example, if the content of the advertisement includes "Buy honda cars at
the lowest prices of the year!", the terms "honda" or "honda cars" may be
extracted from that content. The targeting information may also include
other demographic information, such as geographic location, affluence,
etc. Thus, the targeting information is simply some information from
which a topic may be derived.

[0044]Next, the target document (i.e., the document corresponding to which
a relevant advertisement is requested) is analyzed to identify a topic
corresponding to that target document. (Stage 420). The target document
may be stored on a database 240 or may be provided by ad consumer 130 via
ad consumer interface component 250. There are numerous ways in which the
target document may be analyzed to identify this topic, as described
below in reference to FIG. 5 and related text.

[0045]The targeting information identified in stage 410 is compared to the
one or more topics identified in stage 420 to determine if a match
exists. (Stage 430). A "match" need not be an exact match. Instead, a
match is an indication of a relatively high degree of similarly, and/or a
predetermined (e.g., absolute) degree of similarity. If a match exists,
the advertisement is determined to be relevant to the target document
(stage 440) and may be provided to ad ordering component 270, for
eventual provision to ad consumer 130 via ad consumer interface component
250.

[0046]Those skilled in the art will also recognize that the functions
described in each stage are illustrative only, and are not intended to be
limiting.

[0047]One way to identify a topic corresponding to the target document is
by analyzing some or all text within the target document, which shall be
illustrated in reference to FIG. 5. FIG. 5 shows a sample document,
entitled "Travels in Italy", which contains a collection of
travel-related information pertaining to Italy. The document text
contains the term "restaurant" (appearing 20 times), "chianti" (appearing
10 times), and "the" (appearing 100 times). It could be determined that
one or more of each term (word or phrase) that appears in the title of
the target document corresponds to a topic of the target document. On
this basis, the topics for this document may be "travels", "in", and/or
"italy."

[0048]Alternatively, it could be determined that one or more of each term
that appears in the body of the target document corresponds to a topic of
the target document. In the simplest case, each term within the target
document would be identified as a topic. A slightly more complex approach
would be to identify a term as a topic if it appears in the target
document more than N times, such as N=2 (and indeed such a
threshold-based approach could be used whenever terms within text are
being analyzed). Even more complex analysis could be performed, such as
by using a term vector for the target document, which assigns weights to
each term. For example, terms that appear frequently in the target
document may be assigned a relatively higher weight than those that
appear less frequently. And so the term "the" would have a higher weight
than "restaurant", which would have a higher weight than "chianti".

[0049]In addition, the weighting could be adjusted to give higher weight
to terms that appear less frequently in a collection, such as a
collection to which the document belongs or the general collection of
documents. For example, the term "chianti" does not appear very commonly
across the general collection of documents and so its weight may be
boosted. Conversely, the term "the" appears so frequently across a
collection of documents that its weight may be reduced or eliminated
altogether.

[0050]In any situation where terms within text are assigned weights or
scores, those resulting scores may be used to determine which terms will
be identified as topics for the target document. For example, it may be
determined that only the top scoring term would constitute a topic for
the target document. Alternatively, or in addition, it may be determined
that the top Z terms (or a subset thereof) will constitute topics for the
target document, with Z being some defined number. Alternatively, or in
addition, it may be determined that terms having a score that exceeds Y
(or a subset thereof) will constitute topics for the target document,
with Y being some defined number. Thus, as one skilled in the art will
appreciate, topics may be determined based on absolute and/or relative
criteria.

[0051]Alternatively, or in addition to using text or other information
within the target document, meta-information associated with the target
document may be used. For example, a reference to the target document by
another document may contain a brief description of the target document.
Assume a document called "Entertainment" that contains a reference to the
target document and describes it as "For a description of restaurants and
wine in Italy, see `Travels in Italy`." In the context of a web page,
this is often described as anchor text. One or more such brief
descriptions may be used to revise (figuratively) the target document by
supplementing or replacing some or all of its content with the brief
descriptions. So, for example, the topic could be identified from the
combination of the target document's title and the brief descriptions of
the target document.

[0052]Alternatively, or in addition to the brief descriptions from these
references, the references themselves may be used. For example, a
reference from another document to the target document may be used as an
indication that the two documents are similar. Alternatively, or in
addition, a reference from the target document to another document may be
used as an indication that the two documents are similar. So a reference
between the "Entertainment" document and the "Travels in Italy" document
may indicate that the two are related. In the context of web pages, these
references occur in the form of links from one web page to another. On
this basis, the content (or meta-information) of the other document may
be used to revise (figuratively) the target document by supplementing or
replacing its content with that of the other document. The revised target
document's content may then be analyzed using the techniques described
above to identify one or more topics.

[0053]Alternatively, or addition to using the content (including perhaps
meta-data) associated with a target document, other techniques may be
used to identify one or more topics for the target document. For example,
the top N queries (or subset thereof) that result in a reference to the
target document could be determined to constitute a topic for the target
document, with N being some defined number. These may be, for example,
text queries in a search engine that yield a result that links to the
target document or web page. Alternatively, or in addition, the content
of other similar documents (e.g., in the same collection as the target
document, in the same category as the target document, etc.) may be used
to revise (figuratively) the content of the target document. Any of the
techniques described above may then be used to analyze the target
document to identify one or more topics. In the context of web pages,
this may be other web pages that are stored within a subdirectory of
related pages on the same host as the target web page. Alternatively, or
in addition, any technique for classifying the target document into a set
of one or more topics or categories may be used. Even the search query
history of one or more users who visit the target document (or target web
page) may be used to identify a topic for the target document or web
page, on the theory that a visit to the target document that is
temporally proximate to that search query history indicates that the user
thought the concepts were related. For example, if a user searched for
"italian wine" and then soon afterwards visited the "Travels in Italy"
document, the content of that prior search could be used to determine
that "italian" and/or "wine" are potential topics for the "Travels in
Italy" document.

[0054]Using one or more of the various techniques described above, or
other techniques, one or more topics may be identified for the target
document. Once these topics have been identified, a variety of techniques
may be used to determine other topics that are related to those
identified topics. For example, a thesaurus could be used to determine
other topics (e.g., synonyms) that are closely related to the identified
topics or that are conceptually similar to the identified topics.

[0055]For the sake of clarity, the foregoing references to "revising" the
target document are a figurative aid in understanding the use of
additional information that is not literally within the target document.
Those skilled in the art will recognize that the target document need not
be actually revised to make use of this additional information.

[0056]C. Conclusion

[0057]The foregoing description of preferred embodiments of the present
invention provides illustration and description, but is not intended to
be exhaustive or to limit the invention to the precise form disclosed.
Modifications and variations are possible in light of the above teachings
or may be acquired from practice of the invention.

[0058]The scope of the invention is defined by the claims and their
equivalents.